Overview

Dataset statistics

Number of variables9
Number of observations2018352
Missing cells528
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory138.6 MiB
Average record size in memory72.0 B

Variable types

Numeric8
Text1

Alerts

row_id is uniformly distributedUniform
row_id has unique valuesUnique
county has 212928 (10.5%) zerosZeros
is_business has 934848 (46.3%) zerosZeros
product_type has 170544 (8.4%) zerosZeros
target has 351496 (17.4%) zerosZeros
is_consumption has 1009176 (50.0%) zerosZeros
prediction_unit_id has 30624 (1.5%) zerosZeros

Reproduction

Analysis started2023-12-17 17:56:33.952673
Analysis finished2023-12-17 17:57:17.026318
Duration43.07 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

county
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2970344
Minimum0
Maximum15
Zeros212928
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size15.4 MiB
2023-12-17T18:57:17.190613image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q311
95-th percentile15
Maximum15
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.7809903
Coefficient of variation (CV)0.65519635
Kurtosis-1.2281563
Mean7.2970344
Median Absolute Deviation (MAD)4
Skewness0.023421192
Sum14727984
Variance22.857869
MonotonicityNot monotonic
2023-12-17T18:57:17.424063image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 212928
10.5%
11 198000
9.8%
7 173088
 
8.6%
5 151632
 
7.5%
15 148752
 
7.4%
4 147264
 
7.3%
10 134640
 
6.7%
14 125808
 
6.2%
3 122496
 
6.1%
9 122496
 
6.1%
Other values (6) 481248
23.8%
ValueCountFrequency (%)
0 212928
10.5%
1 91872
4.6%
2 115200
5.7%
3 122496
6.1%
4 147264
7.3%
5 151632
7.5%
6 30624
 
1.5%
7 173088
8.6%
8 91872
4.6%
9 122496
6.1%
ValueCountFrequency (%)
15 148752
7.4%
14 125808
6.2%
13 121056
6.0%
12 30624
 
1.5%
11 198000
9.8%
10 134640
6.7%
9 122496
6.1%
8 91872
4.6%
7 173088
8.6%
6 30624
 
1.5%

is_business
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53682608
Minimum0
Maximum1
Zeros934848
Zeros (%)46.3%
Negative0
Negative (%)0.0%
Memory size15.4 MiB
2023-12-17T18:57:17.718099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.49864212
Coefficient of variation (CV)0.92887088
Kurtosis-1.978185
Mean0.53682608
Median Absolute Deviation (MAD)0
Skewness-0.14770561
Sum1083504
Variance0.24864396
MonotonicityNot monotonic
2023-12-17T18:57:17.963000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1 1083504
53.7%
0 934848
46.3%
ValueCountFrequency (%)
0 934848
46.3%
1 1083504
53.7%
ValueCountFrequency (%)
1 1083504
53.7%
0 934848
46.3%

product_type
Real number (ℝ)

ZEROS 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8989274
Minimum0
Maximum3
Zeros170544
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size15.4 MiB
2023-12-17T18:57:18.205382image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile3
Maximum3
Range3
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0817658
Coefficient of variation (CV)0.56967201
Kurtosis-1.5244696
Mean1.8989274
Median Absolute Deviation (MAD)1
Skewness-0.19921777
Sum3832704
Variance1.1702173
MonotonicityNot monotonic
2023-12-17T18:57:18.437609image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
3 918720
45.5%
1 781632
38.7%
0 170544
 
8.4%
2 147456
 
7.3%
ValueCountFrequency (%)
0 170544
 
8.4%
1 781632
38.7%
2 147456
 
7.3%
3 918720
45.5%
ValueCountFrequency (%)
3 918720
45.5%
2 147456
 
7.3%
1 781632
38.7%
0 170544
 
8.4%

target
Real number (ℝ)

ZEROS 

Distinct565566
Distinct (%)28.0%
Missing528
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean274.85556
Minimum0
Maximum15480.274
Zeros351496
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size15.4 MiB
2023-12-17T18:57:18.776678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.378
median31.133
Q3180.20625
95-th percentile1080.6113
Maximum15480.274
Range15480.274
Interquartile range (IQR)179.82825

Descriptive statistics

Standard deviation909.50238
Coefficient of variation (CV)3.3090194
Kurtosis73.303419
Mean274.85556
Median Absolute Deviation (MAD)31.133
Skewness7.6760621
Sum5.5461015 × 108
Variance827194.58
MonotonicityNot monotonic
2023-12-17T18:57:19.472959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 351496
 
17.4%
0.001 10733
 
0.5%
0.003 6619
 
0.3%
0.002 5365
 
0.3%
0.004 4593
 
0.2%
0.005 2792
 
0.1%
0.1 2316
 
0.1%
0.006 2180
 
0.1%
0.007 1889
 
0.1%
0.015 1861
 
0.1%
Other values (565556) 1627980
80.7%
ValueCountFrequency (%)
0 351496
17.4%
0.001 10733
 
0.5%
0.002 5365
 
0.3%
0.003 6619
 
0.3%
0.004 4593
 
0.2%
0.005 2792
 
0.1%
0.006 2180
 
0.1%
0.007 1889
 
0.1%
0.008 1721
 
0.1%
0.009 1403
 
0.1%
ValueCountFrequency (%)
15480.274 1
< 0.1%
15438.643 1
< 0.1%
15353.106 1
< 0.1%
15332.71 1
< 0.1%
15324.393 1
< 0.1%
15324.196 1
< 0.1%
15323.602 1
< 0.1%
15304.209 1
< 0.1%
15299.353 1
< 0.1%
15271.603 1
< 0.1%

is_consumption
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5
Minimum0
Maximum1
Zeros1009176
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size15.4 MiB
2023-12-17T18:57:19.875791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.50000012
Coefficient of variation (CV)1.0000002
Kurtosis-2.000002
Mean0.5
Median Absolute Deviation (MAD)0.5
Skewness0
Sum1009176
Variance0.25000012
MonotonicityNot monotonic
2023-12-17T18:57:20.124774image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 1009176
50.0%
1 1009176
50.0%
ValueCountFrequency (%)
0 1009176
50.0%
1 1009176
50.0%
ValueCountFrequency (%)
1 1009176
50.0%
0 1009176
50.0%
Distinct15312
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size15.4 MiB
2023-12-17T18:57:20.681367image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters38348688
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-09-01 00:00:00
2nd row2021-09-01 00:00:00
3rd row2021-09-01 00:00:00
4th row2021-09-01 00:00:00
5th row2021-09-01 00:00:00
ValueCountFrequency (%)
05:00:00 84098
 
2.1%
22:00:00 84098
 
2.1%
13:00:00 84098
 
2.1%
11:00:00 84098
 
2.1%
10:00:00 84098
 
2.1%
09:00:00 84098
 
2.1%
08:00:00 84098
 
2.1%
07:00:00 84098
 
2.1%
06:00:00 84098
 
2.1%
04:00:00 84098
 
2.1%
Other values (652) 3195724
79.2%
2023-12-17T18:57:21.528925image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 13610858
35.5%
2 7026302
18.3%
- 4036704
 
10.5%
: 4036704
 
10.5%
1 3325466
 
8.7%
2018352
 
5.3%
3 1227270
 
3.2%
5 566980
 
1.5%
4 559060
 
1.5%
9 545428
 
1.4%
Other values (3) 1395564
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28256928
73.7%
Dash Punctuation 4036704
 
10.5%
Other Punctuation 4036704
 
10.5%
Space Separator 2018352
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13610858
48.2%
2 7026302
24.9%
1 3325466
 
11.8%
3 1227270
 
4.3%
5 566980
 
2.0%
4 559060
 
2.0%
9 545428
 
1.9%
7 465844
 
1.6%
8 465700
 
1.6%
6 464020
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 4036704
100.0%
Other Punctuation
ValueCountFrequency (%)
: 4036704
100.0%
Space Separator
ValueCountFrequency (%)
2018352
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 38348688
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13610858
35.5%
2 7026302
18.3%
- 4036704
 
10.5%
: 4036704
 
10.5%
1 3325466
 
8.7%
2018352
 
5.3%
3 1227270
 
3.2%
5 566980
 
1.5%
4 559060
 
1.5%
9 545428
 
1.4%
Other values (3) 1395564
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38348688
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13610858
35.5%
2 7026302
18.3%
- 4036704
 
10.5%
: 4036704
 
10.5%
1 3325466
 
8.7%
2018352
 
5.3%
3 1227270
 
3.2%
5 566980
 
1.5%
4 559060
 
1.5%
9 545428
 
1.4%
Other values (3) 1395564
 
3.6%

data_block_id
Real number (ℝ)

Distinct638
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean321.8746
Minimum0
Maximum637
Zeros2928
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size15.4 MiB
2023-12-17T18:57:21.772351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34
Q1166
median323
Q3479
95-th percentile606
Maximum637
Range637
Interquartile range (IQR)313

Descriptive statistics

Standard deviation182.63431
Coefficient of variation (CV)0.56740828
Kurtosis-1.1823282
Mean321.8746
Median Absolute Deviation (MAD)157
Skewness-0.018882935
Sum6.4965624 × 108
Variance33355.293
MonotonicityIncreasing
2023-12-17T18:57:22.048682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
452 3312
 
0.2%
450 3312
 
0.2%
436 3312
 
0.2%
437 3312
 
0.2%
438 3312
 
0.2%
439 3312
 
0.2%
440 3312
 
0.2%
441 3312
 
0.2%
442 3312
 
0.2%
443 3312
 
0.2%
Other values (628) 1985232
98.4%
ValueCountFrequency (%)
0 2928
0.1%
1 2928
0.1%
2 2928
0.1%
3 2928
0.1%
4 2928
0.1%
5 2928
0.1%
6 2928
0.1%
7 2928
0.1%
8 2928
0.1%
9 2928
0.1%
ValueCountFrequency (%)
637 3120
0.2%
636 3120
0.2%
635 3120
0.2%
634 3120
0.2%
633 3168
0.2%
632 3168
0.2%
631 3168
0.2%
630 3216
0.2%
629 3216
0.2%
628 3216
0.2%

row_id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct2018352
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1009175.5
Minimum0
Maximum2018351
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size15.4 MiB
2023-12-17T18:57:22.405493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile100917.55
Q1504587.75
median1009175.5
Q31513763.2
95-th percentile1917433.4
Maximum2018351
Range2018351
Interquartile range (IQR)1009175.5

Descriptive statistics

Standard deviation582648.18
Coefficient of variation (CV)0.5773507
Kurtosis-1.2
Mean1009175.5
Median Absolute Deviation (MAD)504588
Skewness-3.629882 × 10-15
Sum2.0368714 × 1012
Variance3.394789 × 1011
MonotonicityStrictly increasing
2023-12-17T18:57:22.777226image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
1345578 1
 
< 0.1%
1345576 1
 
< 0.1%
1345575 1
 
< 0.1%
1345574 1
 
< 0.1%
1345573 1
 
< 0.1%
1345572 1
 
< 0.1%
1345571 1
 
< 0.1%
1345570 1
 
< 0.1%
1345569 1
 
< 0.1%
Other values (2018342) 2018342
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
2018351 1
< 0.1%
2018350 1
< 0.1%
2018349 1
< 0.1%
2018348 1
< 0.1%
2018347 1
< 0.1%
2018346 1
< 0.1%
2018345 1
< 0.1%
2018344 1
< 0.1%
2018343 1
< 0.1%
2018342 1
< 0.1%

prediction_unit_id
Real number (ℝ)

ZEROS 

Distinct69
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.045376
Minimum0
Maximum68
Zeros30624
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size15.4 MiB
2023-12-17T18:57:23.175552image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q116
median33
Q350
95-th percentile64
Maximum68
Range68
Interquartile range (IQR)34

Descriptive statistics

Standard deviation19.590594
Coefficient of variation (CV)0.5928392
Kurtosis-1.211029
Mean33.045376
Median Absolute Deviation (MAD)17
Skewness0.019035511
Sum66697200
Variance383.79137
MonotonicityNot monotonic
2023-12-17T18:57:23.476011image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30624
 
1.5%
46 30624
 
1.5%
32 30624
 
1.5%
33 30624
 
1.5%
1 30624
 
1.5%
35 30624
 
1.5%
36 30624
 
1.5%
37 30624
 
1.5%
38 30624
 
1.5%
39 30624
 
1.5%
Other values (59) 1712112
84.8%
ValueCountFrequency (%)
0 30624
1.5%
1 30624
1.5%
2 30624
1.5%
3 30624
1.5%
4 30624
1.5%
5 30624
1.5%
6 30624
1.5%
7 30624
1.5%
8 30624
1.5%
9 30624
1.5%
ValueCountFrequency (%)
68 3312
 
0.2%
67 23280
1.2%
66 24768
1.2%
65 24768
1.2%
64 26256
1.3%
63 29184
1.4%
62 29184
1.4%
61 29184
1.4%
60 30624
1.5%
59 30624
1.5%

Interactions

2023-12-17T18:57:09.240561image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:40.773172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:44.465950image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:48.373372image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:52.495738image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:56.855764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:57:00.887227image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:57:04.812077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:57:09.693308image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:41.224616image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:44.892336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:48.831761image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:53.010861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:57.296617image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:57:01.295790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:57:05.325040image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:57:10.178418image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:41.698437image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:45.308175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:49.314313image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:53.557355image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:57.739576image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:57:01.782142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:57:05.867897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:57:10.696001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:42.180857image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:45.826072image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:49.800809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:54.096604image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:58.473228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:57:02.279306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:57:06.458982image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:57:11.242610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:42.667615image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:46.382302image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:50.370013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:54.619230image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:58.965313image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:57:02.822774image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:57:07.052594image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:57:11.740781image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:43.092898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:46.846055image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:50.865761image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:55.187990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:59.357704image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:57:03.312534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:57:07.597658image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:57:12.246887image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:43.515663image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:47.350607image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:51.377749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:55.731649image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:59.850626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:57:03.751135image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:57:08.163742image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:57:12.798102image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:43.995291image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:47.893378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:51.945527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:56:56.345618image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:57:00.397176image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:57:04.311589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-17T18:57:08.683559image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2023-12-17T18:57:13.222951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-17T18:57:14.619707image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

countyis_businessproduct_typetargetis_consumptiondatetimedata_block_idrow_idprediction_unit_id
00010.71302021-09-01 00:00:00000
100196.59012021-09-01 00:00:00010
20020.00002021-09-01 00:00:00021
300217.31412021-09-01 00:00:00031
40032.90402021-09-01 00:00:00042
5003656.85912021-09-01 00:00:00052
60100.00002021-09-01 00:00:00063
701059.00012021-09-01 00:00:00073
80110.00002021-09-01 00:00:00084
9011501.76012021-09-01 00:00:00094
countyis_businessproduct_typetargetis_consumptiondatetimedata_block_idrow_idprediction_unit_id
201834215010.00402023-05-31 23:00:00637201834257
2018343150142.40112023-05-31 23:00:00637201834357
201834415032.28702023-05-31 23:00:00637201834458
20183451503117.33212023-05-31 23:00:00637201834558
201834615100.00002023-05-31 23:00:00637201834664
20183471510197.23312023-05-31 23:00:00637201834764
201834815110.00002023-05-31 23:00:00637201834859
2018349151128.40412023-05-31 23:00:00637201834959
201835015130.00002023-05-31 23:00:00637201835060
20183511513196.24012023-05-31 23:00:00637201835160